mypy fixes

mlos_bench/mlos_bench/optimizers/convert_configspace.py

@@ -7,18 +7,24 @@
 """
 
 import logging
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, Hashable, List, Optional, Tuple, Union
 
 from ConfigSpace import (
  Beta,
+ BetaIntegerHyperparameter,
+ BetaFloatHyperparameter,
  CategoricalHyperparameter,
  Configuration,
  ConfigurationSpace,
  EqualsCondition,
  Float,
  Integer,
  Normal,
+ NormalIntegerHyperparameter,
+ NormalFloatHyperparameter,
  Uniform,
+ UniformIntegerHyperparameter,
+ UniformFloatHyperparameter,
 )
 from ConfigSpace.types import NotSet
 
@@ -71,7 +77,9 @@ def _tunable_to_configspace(
  cs : ConfigurationSpace
  A ConfigurationSpace object that corresponds to the Tunable.
  """
- meta = {"group": group_name, "cost": cost} # {"scaling": ""}
+ meta: Dict[Hashable, TunableValue] = {"cost": cost}
+ if group_name is not None:
+ meta["group"] = group_name
 
  if tunable.type == "categorical":
  return ConfigurationSpace(
@@ -102,6 +110,14 @@ def _tunable_to_configspace(
  elif tunable.distribution is not None:
  raise TypeError(f"Invalid Distribution Type: {tunable.distribution}")
 
+ range_hp: Union[
+ UniformFloatHyperparameter,
+ NormalFloatHyperparameter,
+ BetaFloatHyperparameter,
+ UniformIntegerHyperparameter,
+ NormalIntegerHyperparameter,
+ BetaIntegerHyperparameter
+ ]
  if tunable.type == "int":
  range_hp = Integer(
  name=tunable.name,
@@ -120,7 +136,7 @@ def _tunable_to_configspace(
  name=tunable.name,
  bounds=tunable.range,
  log=bool(tunable.is_log),
- distribution=distribution, # type: ignore[arg-type]
+ distribution=distribution,
  default=(
  float(tunable.default)
  if tunable.in_range(tunable.default) and tunable.default is not None